{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook calculates the Planck radiance for the Sun and the Earth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>© Von P. Walden, Washington State University</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from bt2rad import bt2rad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wn = arange(30., 100000., 1.)    # from 0.1 to 333 um, or 100 to 333,333 nm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tsun   = 5900.   #  Units: K\n",
    "Tearth = 300.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118d56f98>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(wn,bt2rad(wn,Tsun))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11953cf98>,\n",
       " <matplotlib.lines.Line2D at 0x11954a128>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(wn,bt2rad(wn,Tsun),'r',wn,bt2rad(wn,Tearth),'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119a132e8>,\n",
       " <matplotlib.lines.Line2D at 0x119a485c0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "semilogx(wn,bt2rad(wn,Tsun)/max(bt2rad(wn,Tsun)),'r',wn,bt2rad(wn,Tearth)/max(bt2rad(wn,Tearth)),'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
